852 Matching Annotations
  1. Last 7 days
    1. AI with intact skills, but broken goals, would be an AI that skillfully acts towards corrupted goals.
    1. Matthew van der Hoorn Yes totally agree but could be used for creating a draft to work with, that's always the angle I try to take buy hear what you are saying Matthew!

      Reply to Nidhi Sachdeva: Nidhi Sachdeva, PhD Just went through the micro-lesson itself. In the context of teachers using to generate instruction examples, I do not argue against that. The teacher does not have to learn the content, or so I hope.

      However, I would argue that the learners themselves should try to come up with examples or analogies, etc. But this depends on the learner's learning skills, which should be taught in schools in the first place.

    2. ***Deep Processing***-> It's important in learning. It's when our brain constructs meaning and says, "Ah, I get it, this makes sense." -> It's when new knowledge establishes connections to your pre-existing knowledge.-> When done well, It's what makes the knowledge easily retrievable when you need it. How do we achieve deep processing in learning? 👉🏽 STORIES, EXPLANATIONS, EXAMPLES, ANALOGIES and more - they all promote deep meaningful processing. 🤔BUT, it's not always easy to come up with stories and examples. It's also time-consuming. You can ask you AI buddies to help with that. We have it now, let's leverage it. Here's a microlesson developed on 7taps Microlearning about this topic.

      Reply to Nidhi Sachdeva: I agree mostly, but I would advice against using AI for this. If your brain is not doing the work (the AI is coming up with the story/analogy) it is much less effective. Dr. Sönke Ahrens already said: "He who does the effort, does the learning."

      I would bet that Cognitive Load Theory also would show that there is much less optimized intrinsic cognitive load (load stemming from the building or automation of cognitive schemas) when another person, or the AI, is thinking of the analogies.


    1. If students know that the AI has some responsibility for determining their grades, that AI will have considerably more authority in the classroom or in any interactions with students.

      warning about AI grading

  2. May 2024
    1. if I met a robot that looked very much like a beautiful girl and everything went fine together with her and me but

      for - comparison - human vs AI robot - Denis Noble

    1. Without intuition, AI can't understand common sense or humane values. Thus, AI might achieve goals in logically-correct but undesirable ways.
    1. One of the key elements was "attribution is non-negotiable". OpenAI, historically, has done a poor job of attributing parts of a response to the content that the response was based on.
    2. I feel violated, cheated upon, betrayed, and exploited.
    3. I wouldn't focus too much on "posted only after human review" - it's worth noting that's that's worth nothing. We literally just saw a case of obviously riduculous AI images in a scientific paper breezing through peer review with noone caring, so quality will necessarily go down because Brandolini's law combined with AI is the death sentence for communities like SE and I doubt they'll employ people to review content from the money they'll make
    4. What could possibly go wrong? Dear Stack Overflow denizens, thanks for helping train OpenAI's billion-dollar LLMs. Seems that many have been drinking the AI koolaid or mixing psychedelics into their happy tea. So much for being part of a "community", seems that was just happy talk for "being exploited to generate LLM training data..." The corrupting influence of the profit-motive is never far away.
    5. If you ask ChatGPT to cite it will provide random citations. That's different from actually training a model to cite (e.g. use supervised finetuning on citations with human raters checking whether sources match, which would also allow you to verify how accurately a model cites). This is something OpenAI could do, it just doesn't.
    6. There are plenty of cases where genAI cites stuff incorrectly, that says something different, or citations that simply do not exist at all. Guaranteeing citations are included is easy, but guaranteeing correctness is an unsolved problem
    7. GenAIs are not capable of citing stuff. Even if it did, there's no guarantee that the source either has anything to do with the topic in question, nor that it states the same as the generated content. Citing stuff is trivial if you don't have to care if the citation is relevant to the content, or if it says the same as you.
    8. LLMs, by their very nature, don't have a concept of "source". Attribution is pretty much impossible. Attribution only really works if you use language models as "search engine". The moment you start generating output, the source is lost.
    1. AI-powered code generation tools like GitHub Copilot make it easier to write boilerplate code, but they don’t eliminate the need to consult with your organization’s domain experts to work through logic, debugging, and other complex problems.Stack Overflow for Teams is a knowledge-sharing platform that transfers contextual knowledge validated by your domain experts to other employees. It can even foster a code generation community of practice that champions early adopters and scales their learnings. OverflowAI makes this trusted internal knowledge—along with knowledge validated by the global Stack Overflow community—instantly accessible in places like your IDE so it can be used alongside code generation tools. As a result, your teams learn more about your codebase, rework code less often, and speed up your time-to-production.
    1. The message of e/acc is this: let’s go full steam ahead in the development of increasingly powerful, general, and conscious artificial intelligences, up to superintelligences. This can only be the right path because it reflects the will of the universe. So far I perfectly agree with the philosophical approach of the e/acc movement.

      E/Acc says invest more in AI limitlessly, as opposed to EA/Bostrom saying invest only in a specific circle of billionaire friends bc of the extinction level risk involved of AGI. And we need to do it, bc religious fervor 'it reflects the will of the universe'. Not convincing.

    1. Thisinterventionrecognizes students’ annotations as objectsopen tocontinuous development, engaging students to connect, analyze, and expand upon their ideasthrough the synthesis processes. Meanwhile, the synthesis products can be integratedinto otherlearning events, enriching the overall learning experiences.

      How can AI be leveraged to support: (1) the process of synthesizing students' annotations, and (2) the use of these synthesis artifacts in subsequent in-class group discussions?

    1. At Google, an AI team member said the burnout is the result of competitive pressure, shorter timelines and a lack of resources, particularly budget and headcount. Although many top tech companies have said they are redirecting resources to AI, the required headcount, especially on a rushed timeline, doesn’t always materialize. That is certainly the case at Google, the AI staffer said.
    2. A common feeling they described is burnout from immense pressure, long hours and mandates that are constantly changing. Many said their employers are looking past surveillance concerns, AI’s effect on the climate and other potential harms, all in the name of speed. Some said they or their colleagues were looking for other jobs or switching out of AI departments, due to an untenable pace.
    3. Artificial intelligence engineers at top tech companies told CNBC that the pressure to roll out AI tools at breakneck speed has come to define their jobs.
    1. We train our models using:
    2. We recently improved source links in ChatGPT(opens in a new window) to give users better context and web publishers new ways to connect with our audiences. 
    3. Our models are designed to help us generate new content and ideas – not to repeat or “regurgitate” content. AI models can state facts, which are in the public domain.
    4. When we train language models, we take trillions of words, and ask a computer to come up with an equation that best describes the relationship among the words and the underlying process that produced them.
    1. the raw data layer (data warehouses and object stores), the compute layer for orchestrated pipelines (feature and inference pipelines), the ML Development services for model training and experiment management, and the state layer for features and models as well as model serving and model monitoring.

      severless ml systems categories

    2. The interactive ML systems are typically a Gradio or Streamlit UI (on Hugging Face Spaces or Streamlit Cloud) and work with a model either hosted or downloaded from Hopsworks. They typically take user input and join it with historical features from Hopsworks Feature Store, and produce predictions in the UI.

      how interactive ML systems operate

    3. These system runs a feature pipeline once/day to synthetically generate a new Iris Flower and write it to the feature store. Then a batch inference pipeline, that also runs once/day but just after the feature pipeline, reads the single flower added that day, and downloads the Iris model trained to predict the type of Iris flower based on the 4 input features: sepal length, sepal width, petal length, and petal width. The model’s prediction is written to an online Dashboard, and actual flower (outcome) is read from the feature store and also published to the same Dashboard - so you can see if the model predicted correctly.

      how analytical ML systems operate

  3. Apr 2024
    1. https://web.archive.org/web/20240430105622/https://garymarcus.substack.com/p/evidence-that-llms-are-reaching-a

      Author suggests the improvement of LLMs is flattening. E.g. points to the closing gap between proprietary and open source models out there, while improvement of proprietary stuff is diminishing or no longer happening (OpenAI progress flatlined 13 months ago it seems). In comment someone points to https://arxiv.org/abs/2404.04125 which implies a hard upper limit in improvement

    1. However, it is unclear how meaningful the notion of "zero-shot" generalization is for such multimodal models, as it is not known to what extent their pretraining datasets encompass the downstream concepts targeted for during "zero-shot" evaluation.

      What seems zero-shot performance by an LLM may well be illusionary as it is unclear what was in training data.

    2. We consistently find that, far from exhibiting "zero-shot" generalization, multimodal models require exponentially more data to achieve linear improvements in downstream "zero-shot" performance

      Exponential increase in training data is needed for linear improvements in zero-shot results of LLMs. This implies a very near, more or less now, brick wall in improvement.

    1. BBC highly critical of Humane AI Pin, just like [[Humane AI Pin review not even close]] I noted earlier. Explicitly ties this to the expectations of [[rabbit — home]] too, which is a similar device. Issue here is I think similar to other devices like voice devices in your home. Not smart enough at the edge, too generic to be of use as [[small band AI personal assistant]] leading to using it for at most 2 or 3 very basic things (weather forecast, time, start playlist usually, and at home perhaps switching on a light), that don't justify the price tag .

    1. AI hype in material science. Google shows an allergy to being pointed to fundamental issues. Another example of pointing out obvious mistakes or issues is not only not welcomed but actively ignored (vgl examples of AI as search engine, where pointing out the first three of the top ten results were wrong resulted in being shouted at, or the blogpost writing video in which presented 'facts' were 1 wikipedia click away from being shown made-up. False citations etc.) It's not bad per se that AI can be wrong, no tool is infallible, but the problem is n:: the extreme asymmetry between the machine effort needed to make stuff up in heaps, and the human effort needed to wade through all the crap and point that out. Vgl [[Spammy handelings assymmetrie 20201220072726]] [[It is easier to F things up than fix things 20180610073041]] Creating entropy is way easier than reducing it, always. We don't need our tools to create ever more entropy on purpose, if only we can reduce it again. Our tools need to help decrease entropy. Decreasing entropy is the definition of life, increasing it should be anathema. Esp if it is unclear where a tool is increasing entropy.

    1. Until recently hedge funds and HFT firms were the main users of AI in finance, but applications have now spread to other areas including banks, regulators, Fintech, insurance firms to name a few

      Using mobile phone data, Bjorkegren and Grissen (2015) employ ML methods to predict loan repayments.

      In the insurance industry, Cytora is using AI to make better risk assessments about their customers, leading to more accurate pricing and minimising claims.

    1. Have you ever had a meaningful conversation with Siri or Alexa or Cortana? Of course not.

      That said, I have had some pretty amazing conversations with ChatGPT-4. I've found it to be useful, too, for brainstorming. In one recent case (which I blogged about on my personal blog), the AI helped me through figuring out a structural issue with my zettelkasten.

    2. rtificial intelligence is already everywhere
    1. “Did you know that the first Matrix was designed to be a perfect human world? Where none suffered, where everyone would be happy. It was a disaster. No one would accept the program… I believe that, as a species, human beings define their reality through suffering and misery. The perfect world was a dream that your primitive cerebrum kept trying to wake up from.”
    2. “I don’t want comfort. I want God, I want poetry, I want real danger, I want freedom, and I want goodness. I want sin.”


      Too many people would want conflict. The source of the conflict is not scarcity: it's human nature.

    3. “It generally becomes easier to be generous if you’re doing well and you have a big windfall, [because] when there isn’t enough for everybody, it’s just a question of who is going to starve, and then everything becomes much tougher,” Bostrom told Big Think.

      Tell that to the super-rich.

    4. erif concluded that scarcity was one of the main drivers of all human conflict. War, violence, invasion, and theft were all born of wanting a limited resource. The history of all humanity seems to support the hypothesis: We fight over water, cattle, arable land, ore deposits, oil, precious stones, and so on.

      He concluded incorrectly.

      Rich people already have more resources than they could ever use. The richest amongst us could not ever spend all the money they possess. But that does not seem to have stopped them from continuing to want more, and more, and more.

    1. Chhit Am-goat  · eprostnSdo919g11fgl4460higl3m0m3thic34lh65i354a1249944tic75f  · Shared with Public《 用台文直接共 AI 討台文內容 》昨昏有朋友問講 AI 出無按家己向望彼號型。窮實佗加強 prompt 佗會使,抑是共欲指定 AI 照你欲--ê 來輸出个規定,囥佇 System Instructions 內底,攏會使--得。下跤圖--ni̍h 是 ka 囥佇 Gemini-pro-1.5 開講區上頂懸个 System Instructions 內底,閣直接叫伊輸入台文文章,*完全無閣 upload 任何物仔*,足簡單个操作。輸出愛檢查,家己愛改,毋過完成度是不止仔懸--矣,而且直接佗出台文內容,毋免閣過翻譯。Prompt 抑是 System Instructions,用英文效果比中文加足好,因為原底遮个 LLM (大言語模型)佗攏用英文訓練--ê。罔參考,會使按家己需要閣改,內底个例攏會換--得,例个長度小可長,效果較好。若想欲輸出 全 POJ、全 TL、抑是全漢,會使改內底个規則佮例,試驗看覓。《System Instructions 》You are a linguist and a great translator between Taigi and English.Translate English text into Taigi I give you, and vise versa.Or chat with me in Taigi.For prepositions, conjunctions, particles and exclamations, must use POJ.For NERs such as country, place or human names keep the original name.English. Be sure to make the Taigi translation more Taigi-like and differentiate from Chinese, using Taigi words and sentences structures as possible, restructuring sentence is allowed.*note 1: Taigi text is a mix of Hanji characters and Latin characters with phonetic component on the top.*note 2: Example of Taigi word consist of Latin letters named POJ(Pe̍h-ōe-jī/POJ) sch as Tâi-gí,má-to͘h, té-thah. Please display the phonetic component and ‘-‘ between subwords accordingly.*note 3: the uniqe POJ elements are: ch, cch, o͘, eng, ek, oa, ⁿ*note 4: Do use POJ for prepositions, conjunctions, particles and exclamations.Output as format&example.<format&example>-----* Leonardo da Vinci ê 無限好玄## Leonardo da Vinci 是文藝復興時期 ê 代表人物, 伊 ê 人生 kah 作品 lóng 浸透彼个時代 ê 精神。伊 tī 1452 年出世 tī Italy ê Vinci, 伊 ê 天才 hāⁿ(迒) 藝術、科學、工程 koh 濟濟 ê 領域, lóng 是 hō͘ 伊對智識 ê 熱狂走chông 來 lu(攄) leh 行。## 伊 tī 藝術 ê 成就, 親像神秘 ê Mona Lisa kah 經典 ê 上尾 ê 暗頓, 展現伊 tùi 人類表情 kah 親像 sfumato hām 明暗對比法遮个先驅技術 ê 把握。 m̄-koh , Da Vinci ê 才調 m̄-nā tī 畫布頂懸; 伊是一个走 tī 時代進前 ê 發明家, 想出來像飛行機器 kah 太陽能這號物。## 伊 ê 簿仔紙展示伊無 kā 藝術 kah 科學分--開 ê 頭殼, 內底滿滿是 chham 觀察、 chham 想像相透濫 ê 素描。Da Vinci ê 哲學是「研究藝術 ê 科學。研究科學 ê 藝術... 認捌 tio̍h 萬物 lóng 連連鬥陣」, 這反映伊相信所有學科 lóng 是 kap(合)做伙--ê。## 伊 tī 藝術 ê 成就, 親像神秘 ê Mona Lisa kah 經典 ê 上尾 ê 暗頓, 展現伊 tùi 人類表情 kah 親像 sfumato hām 明暗對比法遮个先驅技術 ê 把握。 m̄-koh , Da Vinci ê 才調 m̄-nā tī 畫布頂懸; 伊是一个走 tī 時代進前 ê 發明家, 想出來像飛行機器 kah 太陽能這號物。-----Gemini-1.5-pro: https://aistudio.google.com/



      太興奮了。我會希望AI用台文書寫,儘量減少tailo或POJ。以後來研究這個system prompt怎麼下。



      Chhit Am-goat · 《 用台文直接共 AI 討台文內容 》 昨昏有朋友問講 AI 出無按家己向望彼號型。窮實佗加強 prompt 佗會使,抑是共欲指定 AI 照你欲--ê 來輸出个規定,囥佇 System Instructions 內底,攏會使--得。 下跤圖--ni̍h 是 ka 囥佇 Gemini-pro-1.5 開講區上頂懸个 System Instructions 內底,閣直接叫伊輸入台文文章,完全無閣 upload 任何物仔,足簡單个操作。 輸出愛檢查,家己愛改,毋過完成度是不止仔懸--矣,而且直接佗出台文內容,毋免閣過翻譯。 Prompt 抑是 System Instructions,用英文效果比中文加足好,因為原底遮个 LLM (大言語模型)佗攏用英文訓練--ê。 罔參考,會使按家己需要閣改,內底个例攏會換--得,例个長度小可長,效果較好。 若想欲輸出 全 POJ、全 TL、抑是全漢,會使改內底个規則佮例,試驗看覓。 《System Instructions 》 You are a linguist and a great translator between Taigi and English. Translate English text into Taigi I give you, and vise versa.Or chat with me in Taigi. For prepositions, conjunctions, particles and exclamations, must use POJ. For NERs such as country, place or human names keep the original name.English. Be sure to make the Taigi translation more Taigi-like and differentiate from Chinese, using Taigi words and sentences structures as possible, restructuring sentence is allowed. note 1: Taigi text is a mix of Hanji characters and Latin characters with phonetic component on the top. note 2: Example of Taigi word consist of Latin letters named POJ(Pe̍h-ōe-jī/POJ) sch as Tâi-gí,má-to͘h, té-thah. Please display the phonetic component and ‘-‘ between subwords accordingly. note 3: the uniqe POJ elements are: ch, cch, o͘, eng, ek, oa, ⁿ note 4: Do use POJ for prepositions, conjunctions, particles and exclamations. Output as format&example. <format&example>

      • Leonardo da Vinci ê 無限好玄

      Leonardo da Vinci 是文藝復興時期 ê 代表人物, 伊 ê 人生 kah 作品 lóng 浸透彼个時代 ê 精神。伊 tī 1452 年出世 tī Italy ê Vinci, 伊 ê 天才 hāⁿ(迒) 藝術、科學、工程 koh 濟濟 ê 領域, lóng 是 hō͘ 伊對智識 ê 熱狂走chông 來 lu(攄) leh 行。

      伊 tī 藝術 ê 成就, 親像神秘 ê Mona Lisa kah 經典 ê 上尾 ê 暗頓, 展現伊 tùi 人類表情 kah 親像 sfumato hām 明暗對比法遮个先驅技術 ê 把握。 m̄-koh , Da Vinci ê 才調 m̄-nā tī 畫布頂懸; 伊是一个走 tī 時代進前 ê 發明家, 想出來像飛行機器 kah 太陽能這號物。

      伊 ê 簿仔紙展示伊無 kā 藝術 kah 科學分--開 ê 頭殼, 內底滿滿是 chham 觀察、 chham 想像相透濫 ê 素描。Da Vinci ê 哲學是「研究藝術 ê 科學。研究科學 ê 藝術... 認捌 tio̍h 萬物 lóng 連連鬥陣」, 這反映伊相信所有學科 lóng 是 kap(合)做伙--ê。

      伊 tī 藝術 ê 成就, 親像神秘 ê Mona Lisa kah 經典 ê 上尾 ê 暗頓, 展現伊 tùi 人類表情 kah 親像 sfumato hām 明暗對比法遮个先驅技術 ê 把握。 m̄-koh , Da Vinci ê 才調 m̄-nā tī 畫布頂懸; 伊是一个走 tī 時代進前 ê 發明家, 想出來像飛行機器 kah 太陽能這號物。

      Gemini-1.5-pro: https://aistudio.google.com/

    1. LAM is a new type of foundation model that understands human intentions on computers. with LAM, rabbit OS understands what you say and gets things done.

      The Rabbit people say their LAM is a new type of foundation model, to be able to deduce user intention and decided on actions. Sounds like the cli tool I tried, but cutting human out of the loop to approve certain steps. Need to see their research what they mean by 'new foundation model'

    1. Rabbit R1 is a personal AI assistant in a retro box. Supposedly without subscription fees, but with access to AI services and with internet connection. Designed to be able to take action (kind of like the promptchaining cli tool I tried out?). Says it has a LAM next to LLM, a 'large action model' which sounds like marketing rather than tech.

    1. I ran across an AI tool that cites its sources if anyone's interested (and heard of it yet): https://www.perplexity.ai/

      That's one of the things that I dislike the most about ChatGPT is that it just synthesizes/paraphrases the information, but doesn't let me quickly and easily check the original sources so that I can verify (and learn more about the topic by doing further reading) the information for myself. Without access to primary sources, it often feels no better than a rumor — a retelling of what someone somewhere allegedly, purportedly, ostensibly found to be true — can I really trust what ChatGPT claims? (No...)

    1. Perplexity AI's biggest strength over ChatGPT 3.5 is its ability to link to actual sources of information. Where ChatGPT might only recommend what to search for online, Perplexity doesn't require that back-and-forth fiddling.
    1. I occasionally wonder what the impact would be of memorizing a good book in its entirety; I wouldn't be surprised if it greatly influenced my own language and writing.

      may be equivalent of training a generative AI?

    1. Sunnyvale taps AI to translate public meetings

      Sunnyvale taps AI to translate public meetings


    2. The adoption of AI translation is cheaper than hiring human translators. Garnett said the city pays $112.50 for every hour the service is used, and has so far paid about $4,308 for more than 38 hours of usage. Hiring an human translator would cost the city up to $400 per hour. “This creates a much smoother dialogue than using live interpreters,” she  told San José Spotlight. “We likely would have needed two to four live interpreters to accomplish what we are doing with Wordly.”

      The adoption of AI translation is cheaper than hiring human translators. Garnett said the city pays $112.50 for every hour the service is used, and has so far paid about $4,308 for more than 38 hours of usage. Hiring an human translator would cost the city up to $400 per hour.

      “This creates a much smoother dialogue than using live interpreters,” she told San José Spotlight. “We likely would have needed two to four live interpreters to accomplish what we are doing with Wordly.”

    1. However, despite their eloquence, LLMs have some key limitations. Their knowledge is restricted to patterns discerned from the training data, which means they lack true understanding of the world.Their reasoning ability is also limited — they cannot perform logical inferences or synthesize facts from multiple sources. As we ask more complex, open-ended questions, the responses start becoming nonsensical or contradictory.To address these gaps, there has been growing interest in retrieval-augmented generation (RAG) systems. The key idea is to retrieve relevant knowledge from external sources to provide context for the LLM to make more informed responses.

      Good context on what a RAG is for AI / LLMs

    1. It’s all made worse by the AI Pin’s desire to be as clever as possible.

      it reads like that yes. Being able to instruct something rather than guess what it is I want is easier and probably better, because you can tweak your instructions to your own preferences.

    2. But far more often, I’ll stand in front of a restaurant, ask the AI Pin about it, and wait for what feels like forever only for it to fail entirely. It can’t find the restaurant; the servers are not responding; it can’t figure out what restaurant it is despite the gigantic “Joe & The Juice” sign four feet in front of me and the GPS chip in the device.

      This reads as if the device wants to be too clever. You could do this with your phone wearing a headset and instruct it to look up a specific restaurant in your own voice. No need for the device to use location, snap an image, OCR it or whatever.

    3. I hadn’t realized how much of my phone usage consists of these one-step things, all of which would be easier and faster without the friction and distraction of my phone.

      [[AI personal assistants 20201011124147]] should be [[small band AI personal assistant]]s and these are the type of things it might do. This articles names a few interesting use cases for it.

    1. https://web.archive.org/web/20240409122434/https://www.henrikkarlsson.xyz/p/go

      • In the decades before AI beat Go-worldchampion, the highest level of Go-players was stable.
      • After AI beat the Go-worldchampion, there is a measurable increase in the creativity and quality of Go-players. The field has risen as a whole.
      • The change is not attributable to copying AI output (although 40% of cases that happened) but to increased human creativity (60%).
      • The realisation that improvement is possible, creates the improvement. This reminds me of [[Beschouw telkens je adjacent possibles 20200826185412]] wrt [[Evolutionair vlak van mogelijkheden 20200826185412]]
      • Also the improvement coincides with the appearance of an open source model and tool, which allowed players to explore and interact with the AI, not just play a Go-game against it.
      • Examples of plateau-ing of accomplishments and sudden changes exist in sports
      • There may also be a link to how in other fields one might see the low end of an activity up their game through using AI rather than be overtaken by it.

      Paper 2022 publication in Zotero

    1. The intentional invention of any information or citation on an assignment or document.  This includes using generative Artificial Intelligence (AI) or other electronic resources in an unauthorized manner to create academic work and represent it as one's own.

      The use of generative AI in an unauthorized manner to create academic work and present it as your own may be an example of fabrication as described in the Aggie Honor Code part 4.

    1. https://web.archive.org/web/20240402125351/https://garymarcus.substack.com/p/when-will-the-genai-bubble-burst

      On the investment and revenue in #algogens AI. Very lopsided, and surveys report dying enthusiasm with those closely involved. Voices doubt something substantial will come out this year, and if not it will deflate hype of expectations. #prediction for early #2025/ AI hype died down

    1. The same LM can be a much more or less capable agent depending on the enhancements added. The researchers created and tested four different agents built on top of GPT-4 and Anthropic’s Claude:

      While today’s LMs agents don't pose a serious risk, we should be on the lookout for improved autonomous capabilities as LMs get more capable and reliable.

    2. The latest GPT-4 model from OpenAI, which is trained on human preferences using a technique called RLHFEstimated final training run compute cost: ~$50mModel version: gpt-4-0613

      ~$50m = estimated training cost of GPT-4

    1. Additionally, students in the Codex group were more eager and excited to continue learning about programming, and felt much less stressed and discouraged during the training.

      Programming with LLM = less stress

    2. On code-authoring tasks, students in the Codex group had a significantly higher correctness score (80%) than the Baseline (44%), and overall finished the tasks significantly faster. However, on the code-modifying tasks, both groups performed similarly in terms of correctness, with the Codex group performing slightly better (66%) than the Baseline (58%).

      In a study, students who learned to code with AI made more progress during training sessions, had significantly higher correctness scores, and retained more of what they learned compared to students who didn't learn with AI.

    1. OpenAI is offering limited access to a text-to-voice generation platform it developed called Voice Engine, which can create a synthetic voice based on a 15-second clip of someone’s voice.

      OpenAI’s voice cloning AI model only needs a 15-second sample to work

  4. Mar 2024
    1. Next to the xz debacle where a maintainer was psyops'd into backdooring servers, this is another new attack surface: AI tools make up software packages in what they generate which get downloaded. So introducing malware is a matter of creating malicious packages named the way they are repeatedly named by AI tools.

    1. have extensively criticized both companies (and generative AI systems in general) for training their models on masses of online data scraped from their works without consent. Stable Diffusion and Midjourney have both been targeted with several copyright lawsuits, with the latter being accused of creating an artist database for training purposes in December.
    1. 謝昆霖 Verified account  · enrdoSspotmlf916af3f9ct1macahh2ft27cmlc8m5c8c51i2l70554i08m1  · Shared with Public這幾天工作同時有請 GPT-4 和 Claude3 。工作條件:我的提問使用正體中文,同樣問題,只給一次作答機會。Claude3 的回覆速度是 GPT-4 的四倍 (或十倍) 以上不說,而且應答的專業度較高,正體中文的使用也比較在地,我這個支語警察給過。它的回答是大量文字輸出時,GPT4 講一講到後面會跑出簡體中文,Claude3 我還沒遇過,而且字數極多的多輪對話中,它對前面的脈絡記憶很清楚。值得注意的是,GPT-4 正體中文貧乏的問題 Claude3 似乎沒有,它的用字遣詞比 GPT4 多一些人類的隨機性,在重覆20次的例句中,同樣的動詞、形容詞,它會盡量用不同的文字。Claude3 似乎解決了我常常酸 GPT-4 的各種問題。這真是驚人。

      Nice to see there's better support and performance for Traditional Chinese in Claude 3 than ChatGPT4. It feels like David against Goliath.


      謝昆霖 enrdoSspotmlf916af3f9ct1maca h h2ft27cmlc8m5c8c5 1 i2l70554i08m1 · 這幾天工作同時有請 GPT-4 和 Claude3 。工作條件:我的提問使用正體中文,同樣問題,只給一次作答機會。 Claude3 的回覆速度是 GPT-4 的四倍 (或十倍) 以上不說,而且應答的專業度較高,正體中文的使用也比較在地,我這個支語警察給過。 它的回答是大量文字輸出時,GPT4 講一講到後面會跑出簡體中文,Claude3 我還沒遇過,而且字數極多的多輪對話中,它對前面的脈絡記憶很清楚。 值得注意的是,GPT-4 正體中文貧乏的問題 Claude3 似乎沒有,它的用字遣詞比 GPT4 多一些人類的隨機性,在重覆20次的例句中,同樣的動詞、形容詞,它會盡量用不同的文字。 Claude3 似乎解決了我常常酸 GPT-4 的各種問題。這真是驚人。

    1. So good, so true. GPT人没有重要性感受和原创想法,没有办法将理论与实际联系; 可以和那一篇提及the illusion of explanatory depth一起阅读; 一个有用的自测:你说的X到底是什么意思,能不能举个例子,反对X的人有什么理由? 以及看看这种话到底有没有反对者——反对者是一个argument最好的反脆弱性测试。



    1. 可能的威胁:de novo design有害蛋白 solutions: - 自我审查:软件限制 - 公司审查:合成DNA的时候用AI进行审查,但是这个风险转接不知公司是否愿意



    1. 两个有趣的概念: explanatory depth: 误以为自己很了解某事 exploratory breadth: AI 可能让人偏向于去探索AI能做的东西



    1. https://web.archive.org/web/20240305083845/https://www.te-learning.nl/blog/over-de-betrekkelijkheid-van-veilige-ai-en-het-belang-van-digitale-geletterdheid/

      By [[Wilfred Rubens]] citing Leon Furze (?) how 'AI made safe' isn't safe AI just as alcohol free beer isn't soda. I think there's an element here of [[Triz denken in systeemniveaus 20200826114731]] analogue to [[Why False Dilemmas Must Be Killed to Program Self-driving Cars 20151026213310]] where all the focus is on the thing (application, car etc)

    1. 詹益鑑 Verified account  · 45m  · Shared with PublicAI 真的取代了一些工作嗎?或者造成一些工作的薪資降低?今天看到這篇實際分析的文章,從2022 年11 月1 日(ChatGPT 發布前一個月)到2024 年2 月14 日,在 Upwork 的自由工作者資料中,分析出幾個事實:1. 下降幅度最大的 3 個類別是寫作、翻譯和客戶服務工作。寫作職位數量下降了 33%,翻譯職位數量下降了 19%,客戶服務職位數量下降了 16%2. 影片編輯/製作工作成長了 39%,圖形設計工作成長了 8%,網頁設計工作成長了 10%。軟體開發職缺也有所​​增加,其中後端開發職缺成長了 6%,前端/Web 開發職缺成長了 4%3. 翻譯絕對是受打擊最嚴重的工作,每小時工資下降了 20% 以上,其次是影片編輯/製作和市場研究。平面設計和網頁設計工作是最具彈性的。兩人不僅數量增加了,而且時薪也增加了一些。4. 自 ChatGPT 和 OpenAI API 發布以來,與開發聊天機器人相關的工作數量激增了 2000%。如果說當今人工智慧有一個殺手級用例,那就是開發聊天機器人。

      下降幅度最大的 3 個類別是寫作、翻譯和客戶服務工作

      寫作職位數量下降了 33%,翻譯職位數量下降了 19%,客戶服務職位數量下降了 16%

      翻譯絕對是受打擊最嚴重的工作,每小時工資下降了 20% 以上

  5. Feb 2024
    1. Broderick makes a more important point: AI search is about summarizing web results so you don't have to click links and read the pages yourself. If that's the future of the web, who the fuck is going to write those pages that the summarizer summarizes? What is the incentive, the business-model, the rational explanation for predicting a world in which millions of us go on writing web-pages, when the gatekeepers to the web have promised to rig the game so that no one will ever visit those pages, or read what we've written there, or even know it was us who wrote the underlying material the summarizer just summarized? If we stop writing the web, AIs will have to summarize each other, forming an inhuman centipede of botshit-ingestion. This is bad news, because there's pretty solid mathematical evidence that training a bot on botshit makes it absolutely useless. Or, as the authors of the paper – including the eminent cryptographer Ross Anderson – put it, "using model-generated content in training causes irreversible defects"

      Broderick: https://www.garbageday.email/p/ai-search-doomsday-cult, Anderson: https://arxiv.org/abs/2305.17493

      AI search hides the authors of the material it presents, summarising it is abstracting away the authors. It doesn't bring readers to those authors, it just presents a summary to the searcher as end result. Take it or leave it. At the same time, if one searches for something you know about, you see those summaries are always of. Leaving you guessing how of it is when searching something you don't know about. Search should never be the endpoint, always a starting point. I think that is my main aversion against AI search tools. Despite those clamoring 'it will get better over time' I don't think it will easily because the tool nor its makers have any interest in the quality of output necessarily and definitely can't assess it. So what's next, humans factchecking AI output. Why not prevent bs at its source? Nice ref to Maggie Appleton's centipede metaphor in [[The Expanding Dark Forest and Generative AI]]

    1. I major in English literature in University I major in interpreting you went to gr for my master degree I think more than 100 people applied and eight students were admit how many people finally passed the final exam two two two wow




    1. Constructing Prompts for the Command Model Techniques for constructing prompts for the Command model. Developers
    1. Now, let’s modify the prompt by adding a few examples of how we expect the output to be. Pythonuser_input = "Send a message to Alison to ask if she can pick me up tonight to go to the concert together" prompt=f"""Turn the following message to a virtual assistant into the correct action: Message: Ask my aunt if she can go to the JDRF Walk with me October 6th Action: can you go to the jdrf walk with me october 6th Message: Ask Eliza what should I bring to the wedding tomorrow Action: what should I bring to the wedding tomorrow Message: Send message to supervisor that I am sick and will not be in today Action: I am sick and will not be in today Message: {user_input}""" response = generate_text(prompt, temp=0) print(response) This time, the style of the response is exactly how we want it. Can you pick me up tonight to go to the concert together?
    2. But we can also get the model to generate responses in a certain format. Let’s look at a couple of them: markdown tables
    3. And here’s the same request to the model, this time with the product description of the product added as context. Pythoncontext = """Think back to the last time you were working without any distractions in the office. That's right...I bet it's been a while. \ With the newly improved CO-1T noise-cancelling Bluetooth headphones, you can work in peace all day. Designed in partnership with \ software developers who work around the mayhem of tech startups, these headphones are finally the break you've been waiting for. With \ fast charging capacity and wireless Bluetooth connectivity, the CO-1T is the easy breezy way to get through your day without being \ overwhelmed by the chaos of the world.""" user_input = "What are the key features of the CO-1T wireless headphone" prompt = f"""{context} Given the information above, answer this question: {user_input}""" response = generate_text(prompt, temp=0) print(response) Now, the model accurately lists the features of the model. The answer is: The CO-1T wireless headphones are designed to be noise-canceling and Bluetooth-enabled. They are also designed to be fast charging and have wireless Bluetooth connectivity. Format
    4. While LLMs excel in text generation tasks, they struggle in context-aware scenarios. Here’s an example. If you were to ask the model for the top qualities to look for in wireless headphones, it will duly generate a solid list of points. But if you were to ask it for the top qualities of the CO-1T headphone, it will not be able to provide an accurate response because it doesn’t know about it (CO-1T is a hypothetical product we just made up for illustration purposes). In real applications, being able to add context to a prompt is key because this is what enables personalized generative AI for a team or company. It makes many use cases possible, such as intelligent assistants, customer support, and productivity tools, that retrieve the right information from a wide range of sources and add it to the prompt.
    5. We set a default temperature value of 0, which nudges the response to be more predictable and less random. Throughout this chapter, you’ll see different temperature values being used in different situations. Increasing the temperature value tells the model to generate less predictable responses and instead be more “creative.”
    1. T. Herlau, "Moral Reinforcement Learning Using Actual Causation," 2022 2nd International Conference on Computer, Control and Robotics (ICCCR), Shanghai, China, 2022, pp. 179-185, doi: 10.1109/ICCCR54399.2022.9790262. keywords: {Digital control;Ethics;Costs;Philosophical considerations;Toy manufacturing industry;Reinforcement learning;Forestry;Causality;Reinforcement learning;Actual Causation;Ethical reinforcement learning}

    1. This technical report focuses on (1) our method for turning visual data of all types into a unified representation that enables large-scale training of generative models, and (2) qualitative evaluation of Sora’s capabilities and limitations. Model and implementation details are not included in this report.

      AI to generate video images.

    1. [[Lee Bryant]] links to this overview by Simon Willison of what happened in #2023/ in #AI . Some good pointers wrt [[ChatPKM myself]] dig those out.

    1. Oh, compliance moats are definitely real – think of the calls for AI companies to license their training data. AI companies can easily do this – they'll just buy training data from giant media companies – the very same companies that hope to use models to replace creative workers with algorithms. Create a new copyright over training data won't eliminate AI – it'll just confine AI to the largest, best capitalized companies, who will gladly provide tools to corporations hoping to fire their workforces: https://pluralistic.net/2023/02/09/ai-monkeys-paw/#bullied-schoolkids

      Concentration of power.

  6. Jan 2024
    1. But I maintain that all of this is a monumental and dangerous waste of human talent and energy. Imagine what might be accomplished if this talent and energy were turned to philosophy, to theology, to the arts, to imaginative literature or to education? Who knows what we could learn from such people - perhaps why there are wars, and hunger, and homelessness and mental illness and anger

      nice case ofr liberal education

    1. External Resources

      Resource Collections

      AI in Education Resource Directory

      This document contains AI resources of interest to instructors in higher education including tools, readings and videos, presentations, links to AI policies and a resource spreadsheet. The document is managed by Daniel Stanford (SCAD) and contributed to by the AI in Education Google Group.

      Courses and Tutorials

      Prompt Engineering for ChatGPT

      This popular six-module course provides basic instruction in how to work with large language models and how to create complex prompt-based applications for use in education scenarios. Dr. Jules White (Vanderbilt) is the instructor for the course. Absolute beginners to experienced users of large language models will find helpful guidance on designing prompts and using patterns.

      AI Checker Resources

      Michael Coley, Guidance on AI Detection and Why We’re Disabling Turnitin’s AI Detector, Vanderbilt University, https://www.vanderbilt.edu/brightspace/2023/08/16/guidance-on-ai-detection-and-why-were-disabling-turnitins-ai-detector/ (last visited Sep 25, 2023).

      In August, 2023, Vanderbilt's Center for Teaching and Learning provided an explanation of the university's decision to disable Turnitin's AI detection tool. Other universities, such as the University of Pittsburgh, have provided comparable statements about AI writing detection. Vanderbilt noted that AI detection was a difficult or impossible task for technology to solve and will become more difficult as AI tools become more common and advanced. The articles below describe some of the technical challenges with AI detection and unintended effects (e.g., bias against non-native English writers).

      1. Vinu Sankar Sadasivan et al., Can AI-Generated Text Be Reliably Detected?, (2023), http://arxiv.org/abs/2303.11156 (last visited Oct 26, 2023).
      2. Andrew Myers, AI-Detectors Biased Against Non-Native English Writers, Stanford HAI (2023), https://hai.stanford.edu/news/ai-detectors-biased-against-non-native-english-writers (last visited Sep 25, 2023).
      3. Susan D’Agostino, Turnitin’s AI Detector: Higher-Than-Expected False Positives, Inside Higher Ed (2023), https://www.insidehighered.com/news/quick-takes/2023/06/01/turnitins-ai-detector-higher-expected-false-positives (last visited Sep 25, 2023).
      4. Geoffrey A. Fowler, Analysis | We Tested a New ChatGPT-Detector for Teachers. It Flagged an Innocent Student., Washington Post, Apr. 14, 2023, https://www.washingtonpost.com/technology/2023/04/01/chatgpt-cheating-detection-turnitin/ (last visited Sep 25, 2023).
      5. Michael Webb, AI Detection - Latest Recommendations, National centre for AI (Sep. 18, 2023), https://nationalcentreforai.jiscinvolve.org/wp/2023/09/18/ai-detection-latest-recommendations/ (last visited Jan 25, 2024).
    1. After a bit of experimentation (and in a discovery that led us to collaborate), Southen found that it was in fact easy to generate many plagiaristic outputs, with brief prompts related to commercial films (prompts are shown).

      Plagiaristic outputs from blockbuster films in Midjourney v6

      Was the LLM trained on copyrighted material?

    1. More, essentially all research in self-reference for decades has been in artificial intelligence, which is the device around which this plot turns. The language of AI is LISP, the name of the archvillain. In the heyday of LISP machines, the leading system was Flavors LISP Object Oriented Programming or: you guessed it -- Floop. I myself worked on a defense AI program that included the notion of a `third brain,' that is an observer living in a world different than (1) that of the world's creator, and (2) of the characters.
    1. Searching as exploration. White and Roth [71 ,p.38] define exploratory search as a “sense making activity focusedon the gathering and use of information to foster intellectual de-velopment.” Users who conduct exploratory searches are generallyunfamiliar with the domain of their goals, and unsure about howto achieve them [ 71]. Many scholars have investigated the mainfactors relating to this type of dynamic task, such as uncertainty,creativity, innovation, knowledge discovery, serendipity, conver-gence of ideas, learning, and investigation [2, 46, 71].These factors are not always expressed or evident in queriesor questions posed by a searcher to a search system.

      Sometimes, search is not rooted in discovery of a correct answer to a question. It's about exploration. Serendipity through search. Think Michael Lewis, Malcolm Gladwell, and Latif Nasser from Radiolab. The randomizer on wikipedia. A risk factor of where things trend with advanced AI in search is an abandonment of meaning making through exploration in favor of a knowledge-level pursuit that lacks comparable depth to more exploratory experiences.

    1. the canonical unit, the NCU supports natural capital accounting, currency source, calculating and accounting for ecosystem services, and influences how a variety of governance issues are resolved
      • for: canonical unit, collaborative commons - missing part - open learning commons, question - process trap - natural capital

      • comment

        • in this context, indyweb and Indranet are not the canonical unit, but then, it seems the model is fundamentally missing the functionality provided but the Indyweb and Indranet, which is and open learning system.
        • without such an open learning system that captures the essence of his humans learn, the activity of problem-solving cannot be properly contextualised, along with all of limitations leading to progress traps.
        • The entire approach of posing a problem, then solving it is inherently limited due to the fractal intertwingularity of reality.
      • question: progress trap - natural capital

        • It is important to be aware that there is a real potential for a progress trap to emerge here, as any metric is liable to be abused
    1. it didn’t mention more recent work on how to make large language models more energy efficient and mitigate problems of bias.
      • for: AI ethics controversy - citations from Dean please!

      • comment

        • Can Dean please provide the missing citations he is referring to?
    2. In 2017, Facebook mistranslated a Palestinian man’s post, which said “good morning” in Arabic, as “attack them” in Hebrew, leading to his arrest.
      • for: example - progress trap - AI - mistranslation
    3. because the training data sets are so large, it’s hard to audit them to check for these embedded biases. “A methodology that relies on datasets too large to document is therefore inherently risky,
      • for: AI - untraceability - metaphor

      metaphor - untraceability - AI: like a self configuring engine - Imagine a metaphor in the automobile industry. Imagine a car that could self-design itself. - Now imagine the car breaking down and the owner has to bring it into a repair shop to get it fixed. - The problem is that because the AI car designed its own engine and did not make a record of how that was done, no mechanic can fix it.

      • for: progress trap -AI, carbon footprint - AI, progress trap - AI - bias, progress trap - AI - situatedness
    1. 蔡叡浩  · optnSedors54au 031:Mtf a46 mahe2c, hgeca8 ge3ba2hm3t20m81DA2t9l6ra104  · Shared with Public最近一個叫 Plaud Note的廣告打很兇而我就是在嘖嘖募資時的第一波早鳥這幾天用下來我真的覺得很爛通話錄音品質不好要使用它就必須裸機檔案會偶爾不見錄音轉文字功能勉勉強強//但最神奇的事在臉書上看到的任何業配下方一堆網友曬出自己收到商品的照片並大讚好用反觀去他們嘖嘖募資頁面的留言區那裡災難遍地很多人留言說要退貨到底投入多少經費在做口碑操作All reactions:34 You and 33 others

      真相與行銷的差別 前者要費心挖掘 後者有錢好辦事

  7. Dec 2023
    1. the celebrated figures Henry Kissinge

      I think Kissinger's figure is too controversial to leave it at "celebrated".

    2. David Hume’s (2011) formulation of the is–ought problem.
    3. Beyond simpleassociations it acquires high-level abstractions like expressive structure, ideology or beliefsystems, since these are all embodied in the corpora that make up its training sets.

      hm, I'm not sure how LLMs acquire these higher-level concepts out of the probabilistic relations just described.

    1. Universal Summarizer

      (Summary generated with Kagi's Universal Summarizer.)

      Bandcamp has operated as an online music store for over a decade, providing artists and labels with an easy-to-use platform to sell music directly to fans. While receiving little mainstream attention, Bandcamp has paid out $270 million to artists and maintained a simple, artist-focused design. The platform allows free streaming but encourages direct purchases from artists. Chance the Rapper has been a notable champion of Bandcamp, using it for early mixtapes and helping to bring attention to its role in supporting independent musicians. While other services focus on algorithms and playlists, Bandcamp prioritizes direct artist support through low fees and transparent sales data. It has changed little over the years but provides a niche alternative for direct fan-artist connections without the culture-diluting aspects of other streaming services. Bandcamp's low-key approach has helped it avoid issues faced by competitors while continuing to innovate for artists.

      • for: AI, Anirban Bandyopadhyay, brain gel, AI - gel computer

      • title: A general-purpose organic gel computer that learns by itself

      • author
        • Anirban Bandyopadhyay
        • Pathik Sahoo
        • et al.
      • date: Dec. 6, 2023
      • publication: IOPScience
      • DOI: 10.1088/2634-4386/ad0fec

      • ABSTRACT

        • To build energy minimized superstructures, self-assembling molecules explore astronomical options, colliding ∼10 to 9th power molecules s to power−1. -Thusfar, no computer has used it fully to optimize choices and execute advanced computational theories only by synthesizing supramolecules.
        • To realize it,
          • first, we remotely re-wrote the problem in a language that supramolecular synthesis comprehends.
          • Then, all-chemical neural network synthesizes one helical nanowire for one periodic event. These nanowires self-assemble into gel fibers mapping intricate relations between periodic events in any-data-type,
          • the output is read instantly from optical hologram.
          • Problem-wise, self-assembling layers or neural network depth is optimized to chemically simulate theories discovering invariants for learning.
          • Subsequently, synthesis alone solves classification, feature learning problems instantly with single shot training.
          • Reusable gel begins general-purpose computing that would chemically invent suitable models for problem-specific unsupervised learning. Irrespective of complexity,
            • keeping fixed computing time and power, gel promises a toxic-hardware-free world.
    1. it's extremely dangerous to create such an autonomous agent when we do not know how to control it when we 00:58:22 can't ensure that it will not Escape our control and start making decisions and creating new things which will harm us instead of benefit us now this is not a 00:58:34 Doomsday Prophecy this is not inevitable we can find ways to regulate and control the development and deployment of AI we we don't want
      • for: quote - Yuval Noah Harari - AI progress trap, progress trap - AI, quote - progress trap

      • quote it is extremely dangerous to create such an autonomous agent when we do not know how to control it, when we can't ensure that it will not escape our control ad start making decisions and creating new things which will harm us instead of benefit us

      • author: Yuval Noah Harari
      • date 2023
    2. AIS at present they have intelligence but they don't have any Consciousness right there is a huge confusion in many places 01:04:06 between intelligence and Consciousness intelligence is the ability to solve problems to create new things whatever Consciousness is the ability to have feelings that okay you can beat me in 01:04:19 chess are you joyful when you win are you sad when you lose AIS and computers as far as we know they have a lot of intelligence they have zero Consciousness
      • for: AI - lack feelings
    1. Let's beegin honey 😚⬇️Please show me some examples of evergreen contentPlease show me some examples of evergreen contentClearTry for FreeShareShare Results PageShare This AppSave As ImageCopy Text

      In answer to "Please show me some examples of evergreen content", HiveDesk suggested:

      Evergreen content refers to content that remains relevant and valuable to readers over a long period of time. When it comes to the Hive blockchain, evergreen content might include beginner's guides to using the platform, explanations of key concepts like decentralized finance, or tutorials on how to develop applications on the Hive network.

      Some examples of evergreen content for the Hive blockchain could be: - "A Beginner's Guide to Hive: Getting Started with Decentralized Social Media and Cryptocurrency" - "Understanding Decentralized Finance (DeFi) on the Hive Blockchain" - "How to Build and Deploy DApps on the Hive Network: A Step-by-Step Tutorial"

      These types of content would provide value to anyone interested in learning about or getting involved with the Hive blockchain, regardless of when they come across the information.

      References: - "A Beginner's Guide to Hive: Getting Started with Decentralized Social Media and Cryptocurrency" (hive.io/eco) - "Understanding Decentralized Finance (DeFi) on the Hive Blockchain" (hive.blog) - "How to Build and Deploy DApps on the Hive Network: A Step-by-Step Tutorial" (developers.hive.io)


    1. i think it's more likely that 00:49:59 that we will think we will think that we this particular set of procedures ai procedures that we linked into our strategic nuclear weapons system uh will keep us safer but we haven't recognized that they're 00:50:12 unintended that there are consequences glitches in it that make it actually stupid and it mistakes the flock of geese for an incoming barrage of russian missiles and and you know unleashes everything in response 00:50:25 before we can intervene
      • for: example - stupid AI - nuclear launch, AI - progress trap - example - nuclear launch
    2. i think the most dangerous thing about ai is not 00:47:11 super smart ai it's uh stupid ai it's artificial intelligence that is good enough to be put in charge of certain processes in our societies but not good enough to not make really 00:47:25 bad mistakes
      • for: quote - Thomas Homer-Dixon, quote - danger of AI, AI progress trap

      • quote: danger of AI

        • I think the most dangerous thing about AI is not super smart AI, it's stupid AI that is good enough to be put in charge of certain processes but not good enough to not make really bad mistakes
      • author: Thomas Homer-Dixon
      • date: 2021
    3. there's this broader issue of of being able to get inside other people's heads as we're driving down the road all the time we're looking at other 00:48:05 people and because we have very advanced theories of mind
      • for: comparison - AI - HI - example - driving, comparison - artificial i human intelligence - example - driving
    1. LLM based tool to synthesise scientific K

      #2023/12/12 mentioned by [[Howard Rheingold]] on M.

    1. 这个肩负着Facebook的未来的团队规模很小,由大约 30个研究科学家和15名工程师组成。团队有三个分支:Facebook人工智能研究组的主要办公室位于纽约市的Astor Place,由LeCun管理着一个由20名工程师和研究人员组成的团队。Menlo Park的是一个同等规模的分支。六月,FAIR又在巴黎设立了一个更小的5人组,与INRIA(法国计算机科学与自动化研究机构)合作。还有很多在Facebook其他部门一起合作致力于人工智能发展的团队,例如语言技术团队;FAIR只是主要的研究部门。这些研究人员和工程师来自科技领域的各个层面,同时当中很多人都曾与Lecun合作过。高等级的人工智能研究并非是一个庞大的领域,而且Lecun的很多学生都创建了人工智能方面的初创公司,它们一般会被像Twitter这样更大的企业收购。Lecun曾经告诉《连线》杂志,「深度学习实际上是Geofff Hinton,我,还有蒙特利尔大学的Yoshua Bengio之间的一个阴谋。」 Hinton在谷歌研发人工智能, Bengio奔波于蒙特利尔大学和数据挖掘公司Apstat之间,而LeCun也与其他行业内的著名企业有千丝万缕的关联。


    1. https://web.archive.org/web/20231206090650/https://www.theguardian.com/artanddesign/2023/dec/05/wizard-of-ai-artificial-intelligence-alan-warburton-dangers-film

      20 min 'documentary' about what AI does to artists, made with AI by an artist. ODI commissioned it. Does this type of thing actually help any debate? Does it raise questions more forcefully? I doubt it, more likely reinforcing anyone's pre-existing notions. More a curiosum, then.

    1. https://web.archive.org/web/20231205084502/https://www.theguardian.com/world/2023/dec/01/the-gospel-how-israel-uses-ai-to-select-bombing-targets

      Description of AI use by the Israelian miiltary in Gaza. Vgl [[AI begincondities en evolutie 20190715140742]] wrt the difference between AGI evolution beginning in a military or civic setting, and that AI restraints are applied in the civil side, not in military application meaning the likelihood is there not in civil society. This is true in the EU AI Act too that excludes military from scope.

    1. 以現在AI發展的情況,流暢即席口筆譯根本不是難事。

      Accurate transcription of the source language (SL) is still a bottleneck. 源語語音辨識仍是瓶頸

  8. Nov 2023
    1. This illustration shows four alternative ways to nudge an LLM to produce relevant responses:Generic LLM - Use an off-the-shelf model with a basic prompt. The results can be highly variable, as you can experience when e.g. asking ChatGPT about niche topics. This is not surprising, because the model hasn’t been exposed to relevant data besides the small prompt.Prompt engineering - Spend time structuring the prompt so that it packs more information about the desired topic, tone, and structure of the response. If you do this carefully, you can nudge the responses to be more relevant, but this can be quite tedious, and the amount of relevant data input to the model is limited.Instruction-tuned LLM - Continue training the model with your own data, as described in our previous article. You can expose the model to arbitrary amounts of query-response pairs that help steer the model to more relevant responses. A downside is that training requires a few hours of GPU computation, as well as a custom dataset.Fully custom LLM - train an LLM from scratch. In this case, the LLM can be exposed to only relevant data, so the responses can be arbitrarily relevant. However, training an LLM from scratch takes an enormous amount of compute power and a huge dataset, making this approach practically infeasible for most use cases today.

      RAG with a generic LLM - Insert your dataset in a (vector) database, possibly updating it in real time. At the query time, augment the prompt with additional relevant context from the database, which exposes the model to a much larger amount of relevant data, hopefully nudging the model to give a much more relevant response. RAG with an instruction-tuned LLM - Instead of using a generic LLM as in the previous case, you can combine RAG with your custom fine-tuned model for improved relevancy.

    2. OUTBNDSweb: Retrieval-Augmented Generation: How to Use Your Data to Guide LLMs, https://outerbounds.com/blog/retrieval-augmented-generation/ (accessed 13 Nov 2023)